Early detection of dementia in multilingual populations: Visual Cognitive Assessment Test (VCAT)
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of cognitive impairment allows timely intervention with pharmacological and non-pharmacological measures. However, current cognitive evaluation tools do not cater for multilingual populations. OBJECTIVE: To develop and validate a visual-based cognitive evaluation tool, the Visual Cognitive Assessment Test (VCAT), which can be administered to multilingual populations without the need for translation or adaptation. METHOD: We designed a battery of tests to evaluate the domains of memory, executive function, visuospatial function, language and attention. Pilot testing of individual test items, followed by test refinement and development of a field version was performed. We subsequently validated VCAT for the diagnosis of mild cognitive impairment (MCI) and mild Alzheimer's disease (AD). Diagnostic performance was assessed by the area under the curve (AUC), sensitivity (Se) and specificity (Sp). RESULTS: VCAT was validated in a sample of 206 participants. The sample comprised 53.9% males; mean age (SD) was 67.8 (8.86) years; mean years of education was 10.5(6.0). AUC of VCAT for detection of cognitive impairment was found to be 93.3 (95% CI 90.1 to 96.4). Also, the Se and Sp of VCAT for the diagnosis of cognitive impairment (MCI and mild AD) were 85.6% and 81.1%, respectively. VCAT's diagnostic Se and Sp comparable to those of the Montreal Cognitive Assessment in the same cohort. Mean time-to-complete VCAT was 15.7 ± 7.3 min. CONCLUSIONS: The VCAT has good Se and Sp for the diagnosis of MCI and mild AD. The visual-based test paradigm allows easy application to multilingual populations without the need for translation or adaptation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".